code: def forward(self, inputs: torch.Tensor, mask=None):
      # inputs.shape = [batch_size, seq_len, embedding_dim]
      batch_size = inputs.shape[0]
      query = self.query_dense(inputs)  # (batch_size, seq_len, h)
      key = self.key_dense(inputs)  # (batch_size, seq_len, h)
      value = self.value_dense(inputs)  # (batch_size, seq_len, h)
      query = self.separate_heads(
          query, batch_size
      )  # (batch_size, num_heads, seq_len, projection_dim)
      key = self.separate_heads(
          key, batch_size
      )  # (batch_size, num_heads, seq_len, projection_dim)
      value = self.separate_heads(
          value, batch_size
      )  # (batch_size, num_heads, seq_len, projection_dim)
      outputs, weights = self.attention(query, key, value, mask=mask)
      outputs = torch.permute(
          outputs, (0, 2, 1, 3)
      )  # (batch_size, seq_len, num_heads, projection_dim)
      concat_outputs = torch.reshape(
          outputs, (batch_size, -1, self.embedding_size)
      )  # (batch_size, seq_len, h)
      projected_outputs = self.combine_heads(
          concat_outputs
      )  # (batch_size, seq_len, h)
      return projected_outputs
  
imports: import logging
  import torch
  from torch import nn
  from torch.nn import functional
  from ludwig.utils.torch_utils import get_activation, LudwigModule
label_desc: Use image node_id 4 for calling the MultiHeadSelfAttention obj's underlying member method code with example usage: obj.forward(inputs, mask) and returns: projected_outputs